Hellinger Distance Trees for Imbalanced Streams

R.J. Lyon, J.M. Brooke, Joshua Knowles, B.W. Stappers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)
232 Downloads (Pure)


Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the learning objective is rare class identification. As accuracy may provide a misleading impression of performance on imbalanced data, existing stream classifiers based on accuracy can suffer poor minority class performance on imbalanced streams, with the result being low minority class recall rates. In this paper we address this deficiency by proposing the use of the Hellinger distance measure, as a very fast decision tree split criterion. We demonstrate that by using Hellinger a statistically significant improvement in recall rates on imbalanced data streams can be achieved, with an acceptable increase in the false positive rate.
Original languageEnglish
Title of host publicationProceedings 22nd International Conference on Pattern Recognition (ICPR), 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781479952083
Publication statusPublished - Aug 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014


Conference22nd International Conference on Pattern Recognition, ICPR 2014


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